---
id: "AEB-010"
category: "context_scaling"
confidence: 0.96
date: "2026-02-15"
source_file: "Agentic OS Blueprint Research Synthesis (Active Memory section, arXiv:2512.24601)"
---

# AEB-010

The Recursive Language Model (RLM) approach treats massive datasets as environments to be searched rather than data to be memorized -- when facing a million-token document, an RLM writes code to spawn sub-agents (Agent A checks index, Agent B reads sections, Root Model synthesizes), scaling to 10M+ tokens while outperforming base LLMs by 2x on information-dense tasks at linear token costs

## Relationships

